code: def convert_to_tensor(x, dtype=None, sparse=None):
      if sparse:
          raise ValueError(
              "`sparse=True` is not supported with numpy backend"
          )
      if dtype is not None:
          dtype = standardize_dtype(dtype)
      if isinstance(x, Variable):
          if dtype and dtype != x.dtype:
              return x.value.astype(dtype)
          return x.value
      if dtype is None:
          dtype = result_type(
              *[
                  getattr(item, "dtype", type(item))
                  for item in tree.flatten(x)
              ]
          )
      return np.array(x, dtype=dtype)
  
imports: import numpy
  import tree
  from keras.backend.common import KerasVariable
  from keras.backend.common import standardize_dtype
  from keras.backend.common.dtypes import result_type
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_tensor(x, dtype, sparse) and returns: np, x, x